Although our proposed DMR can handle a variety of fusion settings, we observe that the DMR results may have visual artifacts. This is due to the ill-posedness of the fusion problem and different noise levels between the two sensing modalities. In order to address these issues, we model the artifacts outcome of DMR as additive “noise” and propose a “denoising” process to remove the artifacts. Inspired by ResNet [1]} and DnCNN [2]}, we employ the residual learning scheme and train a residual denoiser (RD). Rather than training the denoiser from various levels of artificial noise, we design to train the network from the outcome of DMR. Mathematically, the residual \({R}\)  is expressed as,
\({R} = \hat{{H}} - {H}_g,\) 
